The field of the present invention relates to presenting television advertisements that are targeted based on online user profiles. In particular, systems and methods are described for presenting targeted television advertisements, selected on the basis of an online user profile, in association with television programs or channels for which a relationship has been identified with that online user profile.
A variety of systems and methods currently target advertisements based on user/viewer/customer behavior. Many of those rely on collection of personally identifiable information (PII) to correlate the person exhibiting the behavior with advertisements targeted at that person. In some systems, advertisements can be targeted without collecting PII, but in such systems, typically, the advertisement is delivered over the same medium as the medium in which the non-personally identifiable information is measured or collected. For example, many grocery stores hand out so-called “club cards,” which can but need not be linked to PII. A shopper presents the card at checkout to receive various discounts, thereby allowing the store to link a list of purchased items to the card. As the system “learns” the purchasing habits of that cardholder, it can begin issuing coupons targeted at purchases that the cardholder has made previously or that the system predicts the cardholder may wish to make based on past purchases. In another example, online advertisements can be targeted based on an Internet user's online activities without using PII. The use of browser cookies enables an ad server to recognize an Internet site visitor (more accurately, the computer or other device used by the visitor) who has previously conducted searches, accessed content, or viewed ads at the same or a different site linked to the ad server. The ad server can target future advertising to the site visitor based on that previous activity, again without necessarily using PII. A user who has searched for airline tickets to southern California on an online travel site, for instance, might later receive targeted online advertisements for Disneyland, which the ad server (the one that collected or received the user's search information from the online travel site) delivers to the user's computer, perhaps while the user is visiting another online site.
Targeting of advertisements becomes significantly more problematic to deliver “cross-medium,” i.e., when an advertisement is presented via one medium based on user behavior exhibited, or demographic information learned, in another medium. One example of cross-medium advertising is presentation of television advertisements that are targeted based on an online user profile. One difficulty, however, arises from the need to associate an online access device (e.g., a computer connected to the Internet) and a corresponding television device (e.g., a set-top box). Use of PII can facilitate the proper association. A few methods have been developed to associate computer and TV units without using PII, including, for example, those disclosed in U.S. application Ser. Nos. 11/736,544 (entitled “Targeted television advertisements based on online behavior” filed Apr. 17, 2007 in the name of Roy Shkedi) and 11/968,117 (entitled “Targeted online advertisements based on viewing or interacting with television advertisements” filed Dec. 31, 2007 in the names of Roy Shkedi and Ronen Shlomo), both of which applications being hereby incorporated by reference in their entirety. Other methods for establishing such associations may exist or may be developed in the future. However the association is made (with or without PII), information from an online user profile collected or generated during computer access of the Internet in a household can be used to select a targeted television advertisement, which can be presented via the set-top box in the household that is associated with the computer.
A problem remains, however, because a household may include multiple users, each of whom independently access online content and independently watch television. A targeted advertisement selected based on online user profile information for a first household member might be wasted if presented to a second household member, especially one for whom the targeted advertisement might not be suitable or effective. For example, a television advertisement for investment advice targeted based on the ad server having recognizing that Dad had checked his online investment account would be wasted if presented on television while Daughter watches a pop music show, even though the ad server has determined that Dad's computer is associated with the set-top box connected to the television watched by Daughter.
It is therefore desirable to provide systems and methods for increasing the probability that a television advertisement targeted based on an online user profile is presented while the corresponding user (i.e., the “target”) watches television.